Tactical

In contemplating the future of government-supported AI efforts, particularly with initiatives like Stargate, there emerges a striking parallel to the decentralized ethos of cryptocurrency. The essence of this comparison lies in the democratization of access to computational resources and AI models, much like the way cryptocurrencies aim to distribute financial power more equitably. The Stargate project, with its monumental $500 billion investment, is not just a leap towards technological advancement but also poses a philosophical question about control, access, and the human elements that will inevitably color its outcomes.

The neural network model described, with its layers from the external world to physical actions, mirrors the journey of AI from data input to real-world application. It suggests a system where AI processes information through layers akin to human cognitive functions—perception, decision-making, and action in the physical world. This model, with its nodes like ‘Competition-Blockchain’ and ‘Freedom-Crypto’, hints at a future where AI could be as decentralized as cryptocurrency, where decisions are made in open systems rather than closed, controlled environments. Here, AI could empower individuals by providing API access or computational resources, much like blockchain offers transparency and control over financial transactions.

Fig. 1 Got your hands on every recording by Hendrix, Joni Mitchell, CSN, etc (foundations)? Thou shalt inherit the kingdom (yellow node)!

However, this vision is fraught with both known and unknown challenges, reflecting our very human nature. The known challenges include privacy concerns, security, and the management of such vast computational resources in a fair manner. Unknown challenges might involve the unpredictable evolution of AI, the ethical dilemmas of AI decision-making, or how AI might be weaponized or monopolized. These issues echo the concerns in the cryptocurrency space about regulation, security, and economic disparity.

The announcement of Stargate under the Trump administration, amidst the backdrop of political and tech industry rivalries, underscores the tension between innovation and control. The involvement of high-profile tech leaders like Sam Altman and Elon Musk, with their public spats and differing visions of AI’s future, illustrates the personal stakes and ideological battles that could shape this project. Their disagreements mirror the broader societal debate on whether AI should be a centralized, government-backed endeavor or a more open, community-driven development akin to the ethos of cryptocurrency.

What’s confusing about this is it’s not clear that this is funding breakthrough research.
– Ethan Mollick

If AI is to proceed in a way that resembles cryptocurrency, it would mean embracing principles of transparency, decentralization, and perhaps most importantly, accessibility. This approach could mitigate some of the risks associated with centralized AI control, like the potential for misuse by a single entity or government. However, it would also require a robust framework for governance, much like the regulations that are slowly being shaped around cryptocurrencies, to ensure that this powerful technology benefits society at large rather than just a few.

In essence, the way forward for government-supported AI efforts must navigate this complex landscape of technological promise versus human complexity. By learning from the decentralized and transparent nature of cryptocurrency, there’s potential to create an AI ecosystem that not only advances our capabilities but also aligns with democratic ideals of openness and participation. The journey of AI from perception to physical reality, as depicted in the neural network, should ideally reflect a path where technology serves humanity’s collective needs, not just the interests of its creators or controllers.

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import numpy as np
import matplotlib.pyplot as plt
import networkx as nx

# Define the neural network fractal
def define_layers():
    return {
        'World': ['Cosmos-Entropy', 'Planet-Tempered', 'Life-Needs', 'Ecosystem-Costs', 'Generative-Means', 'Cartel-Ends', ], ## Cosmos, Planet
        'Perception': ['Perception-Ledger'], # Life
        'Agency': ['Open-Nomiddleman', 'Closed-Trusted'], # Ecosystem (Beyond Principal-Agent-Other)
        'Generative': ['Ratio-Weaponized', 'Competition-Tokenized', 'Odds-Monopolized'], # Generative
        'Physical': ['Volatile-Distributed', 'Unknown-Players',  'Freedom-Crypto', 'Known-Transactions', 'Stable-Central'] # Physical
    }

# Assign colors to nodes
def assign_colors():
    color_map = {
        'yellow': ['Perception-Ledger'],
        'paleturquoise': ['Cartel-Ends', 'Closed-Trusted', 'Odds-Monopolized', 'Stable-Central'],
        'lightgreen': ['Generative-Means', 'Competition-Tokenized', 'Known-Transactions', 'Freedom-Crypto', 'Unknown-Players'],
        'lightsalmon': [
            'Life-Needs', 'Ecosystem-Costs', 'Open-Nomiddleman', # Ecosystem = Red Queen = Prometheus = Sacrifice
            'Ratio-Weaponized', 'Volatile-Distributed'
        ],
    }
    return {node: color for color, nodes in color_map.items() for node in nodes}

# Calculate positions for nodes
def calculate_positions(layer, x_offset):
    y_positions = np.linspace(-len(layer) / 2, len(layer) / 2, len(layer))
    return [(x_offset, y) for y in y_positions]

# Create and visualize the neural network graph
def visualize_nn():
    layers = define_layers()
    colors = assign_colors()
    G = nx.DiGraph()
    pos = {}
    node_colors = []

    # Add nodes and assign positions
    for i, (layer_name, nodes) in enumerate(layers.items()):
        positions = calculate_positions(nodes, x_offset=i * 2)
        for node, position in zip(nodes, positions):
            G.add_node(node, layer=layer_name)
            pos[node] = position
            node_colors.append(colors.get(node, 'lightgray'))  # Default color fallback

    # Add edges (automated for consecutive layers)
    layer_names = list(layers.keys())
    for i in range(len(layer_names) - 1):
        source_layer, target_layer = layer_names[i], layer_names[i + 1]
        for source in layers[source_layer]:
            for target in layers[target_layer]:
                G.add_edge(source, target)

    # Draw the graph
    plt.figure(figsize=(12, 8))
    nx.draw(
        G, pos, with_labels=True, node_color=node_colors, edge_color='gray',
        node_size=3000, font_size=9, connectionstyle="arc3,rad=0.2"
    )
    plt.title("AI Meets Crypto", fontsize=15)
    plt.show()

# Run the visualization
visualize_nn()

Fig. 2 Tokens are digital assets (keys) to open up a ledger (yellow). What exactly the ledger is about is another topic altogether. But it might be API, block chain, or access to Microsofts Azure. In essence, the way forward for government-supported AI efforts must navigate this complex landscape of technological promise versus human complexity. By learning from the decentralized and transparent nature of cryptocurrency, there’s potential to create an AI ecosystem that not only advances our capabilities but also aligns with democratic ideals of openness and participation. The journey of AI from perception to physical reality, as depicted in the neural network, should ideally reflect a path where technology serves humanity’s collective needs, not just the interests of its creators or controllers. The thing to me that’s always been weird about this is that no one’s articulated a vision for what the world will look like. They’re always willing to say it comes down to supercharged scientific research, which may very well be true, but also requires rethinking how science is organized to take advantage of these models. What does that look like? There are a lot of other social components that have to fit into it